Course Code: llmautogen
Duration: 21 hours
Prerequisites:
  • An understanding of Python programming
  • Familiarity with large language models and prompt engineering
  • Experience with APIs and automation workflows

Audience

  • AI engineers
  • ML developers
  • Automation architects
Overview:

Building LLM Agent Systems with AutoGen is a hands-on course focused on developing multi-agent systems using Microsoft’s AutoGen framework for large language models (LLMs).

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI and automation professionals who wish to design, implement, and orchestrate multi-agent systems using AutoGen with Python and LLMs.

By the end of this training, participants will be able to:

  • Design multi-agent architectures using the AutoGen framework.
  • Configure agent roles, capabilities, and coordination behaviors.
  • Use function-calling and memory handling for agent interactions.
  • Build and test Python-based LLM agent workflows for real use cases.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to LLM Agent Systems

  • LLM agents and multi-agent architecture concepts
  • Overview of AutoGen framework and ecosystem
  • Agent roles: user proxy, assistant, function caller, and more

Installing and Configuring AutoGen

  • Setting up the Python environment and dependencies
  • AutoGen configuration file basics
  • Connecting to LLM providers (OpenAI, Azure, local models)

Agent Design and Role Assignment

  • Understanding agent types and conversation patterns
  • Defining agent goals, prompts, and instructions
  • Role-based task delegation and control flow

Function Calling and Tool Integration

  • Registering functions for agent use
  • Autonomous and collaborative function execution
  • Connecting external APIs and Python scripts to agents

Conversation Management and Memory

  • Session tracking and persistent memory
  • Agent-to-agent messaging and token handling
  • Managing conversation context and history

End-to-End Agent Workflows

  • Building multi-step collaborative tasks (e.g., document analysis, code review)
  • Simulating user-agent dialogues and decision chains
  • Debugging and refining agent performance

Use Cases and Deployment

  • Internal automation agents: research, reporting, scripting
  • External-facing bots: chat assistants, voice integrations
  • Packaging and deploying agent systems in production

Summary and Next Steps

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